METHOD FOR PREDICTION OF PHYSICAL PROPERTY DATA OF HYDROCARBON PRODUCTS

Abstract: A method for data processing and optimisation of a neural network for application in the determination of physical property data of hydrocarbon products from measured (N.)I.R. spectral absorbances, characterized by the steps of: a) measuring the (N.)I.R. spectra of a large set of hydrocarbon product samples from a wide range of sources; b) selecting an overtone (harmonic) region of the (near-)infrared spectra, thus obtained; c) selecting a number of discrete wavelengths in each (N.)I.R. spectrum, converting a number of the said wavelengths to absorption data and using said absorption data as an input to a neural network; d) training the neural network on the entire data set by repeated presentation of inputs and known outputs i.e. the near-infrared data for the hydrocarbon product and its relevant physical property data, to learn the relationship between the two, and monitoring the performance of its predicitions against the actual physical property data as measured by standard methods for the training data, thus correlating the absorbance values with said relevant physical property; e) generating a set of values of the interconnection weights and biases of the network as adjusted after the learning period of step d); and f) applying these adjusted values, utilizing the network algorithm to (near-)infrared spectra, taken under the same conditions, for hydrocarbon products of unknown physical property data.

Description: METHOD FOR PREDICTION OF PHYSICAL PROPERTY DATA OF HYDROCARBON PRODUCTS The present invention relates to a method for prediction of physical property data of a hydrocarbon product by correlation of its (near-)infra-red ((N.)I.R.) spectrum to said physical property in question by means of neural networks. Such physical properties include octane number, vapour pressure, cetane number, density and the like. In particular, mid-I.R. (3-14 μm) is used for the determination of cetane ignition improver concentration. As known to those skilled in the art, organic compounds have in the infra-red region (about 1 to about 300 μm) a unique spectral fingerprint. Recent market trends for hydrocarbon products such as bulk fuels put more onus on the quality and performance characteristics of the product. Whole marketing campaigns are being fought to convince the customer that one fuel noticeably performs better than any other, and this philosophy is expected to continue. Such an approach needs to be backed up by full quality maintenance , from refining through blending and finally to delivery to the customer. This requirement can be best met by using quality monitoring instruments to measure performance characteristics of the fuel. At the moment, after leaving the refinery the checks made on the fuel to see if it still is within specification could be improved: in the case of gasoline it is only manually checked for clarity, smell and has its density noted. This, taking into account the highly complex nature of the modern advanced gasolines is not enough. A quality monitoring instrument package which actually measures performance specification of fuels would be advantageous , so degradation to the fuel, cross contamination with other fuels, and changes in the fuels composition could be detected and pinpointed in the distribution chain. This will not only ensure the customer receives the fuel exactly as intended, but problems along the distribution chain may be found and addressed. The most important performance measure of gasoline is octane number (O.N.), this is presently measured by a standard engine at the laboratories of the refineries. This engine is an expensive piece of equipment to install, requires frequent maintenance and calibration, and needs highly trained personnel to operate and analyse results. Labour and costs prohibit the installation of such engines along the distribution chain and therefore a cheaper alternative to measure octane number needs to be found. A similar performance measure of Diesel oil is cetane number, which is a quantity reflecting the ignition properties of diesel oil. Engines do exist for cetane number measurement. Reference fuels are used in the same way as for the octane number determination for motor gasoline. These reference fuels are n-cetane and α-methyl naphthalene. The ignition quality is expressed as the cetane number, which is the percentage by volume of cetane in a blend with α-methyl naphthalene whose ignition performance matches that of the fuel in the test engine. In practice, cetane engines are seldom used, and cetane is normally calculated as "cetane index" from other measurements e.g. density and distillation. The cetane index, however, may not be suitable for diesel fuels of the future, and an alternative measurement of cetane number is required. It has already been proposed to determine in principle the octane number of an unleaded gasoline as a function of near-infra-red (N.I.R.) spectra, applying a neural network. However, such a prediction of the octane number of a hydrocarbon product such as gasoline from its N.I.R. spectrum is susceptible of improvement and it is therefore an object of the invention to provide a method for accurate prediction of octane number of unknown gasolines (both leaded and unleaded) , wherein the octane number of blended product and of process streams with a wide range of octane numbers can be predicted. Similar needs exist with respect to improvement of prediction with respect to other physical properties of hydrocarbon products, e.g. vapour pressure and density and cetane number. Further, it has already been proposed to determine the cetane number of gasoil as a function of near-infrared (N.I.R.) spectra. (Vide e.g. EP-A-0,304,232. ) However, until now it was not possible to predict the cetane number of a gasoil from its I.R. spectrum. The invention therefore provides a method for data processing and optimisation of a neural network for application in the deter- mination of physical property data of hydrocarbon products from measured (N.)I.R. spectral absorbances, characterized by the steps of: a) measuring the (N.)I.R. spectra of a large set of hydrocarbon product samples from a wide range of sources; b) selecting an overtone (harmonic) region of the (N.)I.R. spectra, thus obtained; c) selecting a number of discrete wavelengths in each (N.)I.R. spectrum, converting a number of the said wavelengths to absorption data and using said absorption data as an input to a neural network; d) training the neural network on the entire data set by repeated presentation of inputs and known outputs i.e. the (near-)infra-red data for the hydrocarbon product and its relevant physical property data, to learn the relationship between the two, and monitoring the performance of its predictions against the actual relevant physical property data as measured by standard methods for the training data, thus correlating the absorbance values with said relevant physical property; e) generating a set of values of the interconnection weights and biases of the network as adjusted after the learning period of step d) ; and f) applying these adjusted values, utilizing the network algorithm, to (near-)infra-red spectra, taken under the same conditions, for hydrocarbon products of unknown physical property data. Advantageously, 0.78-30 μm wavelength is applied. More advantageously, near-infra-red (0.7-3 μm wavelength) is applied. The invention will now be described in more detail by way of example by reference to the accompanying drawing, in which the figure represents schematically a neural network advantageously applied by the method of the invention. The invention is based upon the principle of correlation of physical properties of hydrocarbon products such as octane number, vapour pressure, density and the like, to near-infra-red spectrum. This principle is known as such (vide e.g. EP-A-0,304,232 and EP-A-0,285,251) and will not be described in detail. Further, a neural network is applied. The general theory and general operation of neural networks as such is known to those skilled in the art and will therefore not be described in detail. Generally, a neural network can be defined as a system, wherein during a learning period a correlation between input- and output variables is searched for. After sufficient examples have been offered in this learning period the neural network is able to produce the relevant output for an arbitrary input. Neural networks have found applications e.g. for pattern recognition problems. As those skilled in the art will appreciate, neural networks are built up of layers of processing elements (similar to the brain's neurons) each of which is weighted and connected to elements in other layers (similar to the brain's synapses). A network learns patterns by adjusting weights between the elements whilst it is being trained with accurate qualified data. According to an advantageous learning algorithm, training errors, the difference between the actual and predicted result are propagated backwards through the network to the hidden layers which receive no feedback from training patterns. The weights of the interconnections are adjusted in small steps in the direction of the error, to minimize the errors, and the training data is run through again. This happens many times till the error reaches an acceptable level, which is usually the repeatability of the initial measurement. In the following, the invention will particularly be described referring to the prediction of octane number of gasoline, but it will be appreciated by those skilled in the art that the invention is not restricted thereto and could also be used for prediction of vapour pressure, density, cetane number and the like. According to the invention the (near-)infra-red ((N.)I.R.) spectra of a large set of gasoline samples (advantageously at least 100) , from a wide range of sources are measured, covering different crude feedstocks and intermediate refinery processes. The variety is important since this determines the generality and applicability of any subsequent predictive tool. Advantageously, the second overtone (harmonic) region of the (N.)I.R. spectrum is chosen. This region covers 900-1300 nm (wavelength) and is chosen as it is in this region that the best balance between available information from the measurement and component instrumentation stability and sensitivity can be achieved. A number of discrete wavelengths is converted to absorption data, which are used as the input to a neural network. Data analysis on the set of spectra corresponding to the gasolines of the training set is done in the following manner: 1. The mean spectrum of the set is generated and the differences between each individual spectrum and the mean are calculated. 2. The mean spectrum will be in the order of 5000 data points and so the problem of analysis of a set of 100 gasolines is very difficult. A technique is required to allow data reduction to a manageable number of problem variables. 3. In the case of neural network technology the data reduction is performed by physical reduction in the number of measured wavelengths. The data reduction is in the following manner: A multivariate statistical technique such as e.g. Principal Component Analysis is used on the training set of gasoils, to generate a 'property spectrum' which represents the relative importance of each spectral data point to the correlation with octane number. The spectral measurement is then simplified to discrete wavelengths, typically numbering between 5 and 10. The absorbance values are used as the input to the neural network. Advantageously, the number of selected wavelengths is 5 for fuels that do not contain alcohols but may contain oxygenates e.g. MTBE and 6 if the fuels do contain alcohol. Advantageously, for cetane number measurement a wavelength of 6-7 μm is chosen in addition to monitor the concentration of cetane ignition improver additive. One of the wavelengths is advantageously used as a transmission reference to correct for any instrumental drifts. The remaining wavelengths, corrected by the reference, are converted to absorption data. This may be done logarithmically, and the data can be mathematically scaled within predetermined bounds for each wavelength. That is, extreme values expected for either fuels, or more likely, process streams are used to provide the range of acceptable absorbances at each wavelength against which the scaling can be done for the fuel to be tested. The neural network is trained on the entire data set by repeated presentation of input and known outputs i.e. the infra-red data for a gasoline and its octane number, to learn the relationship between the two and the performance of its predictions against the actual octane number data as measured by standard engine methods is monitored. Once the neural network has "learned" the relationship, the data set should be split into a further training set and a validation set that will not be used in the "learning" phase. Advantageously, as shown in the figure the network used has a three-layer architecture which, for example, comprises in a first layer four input nodes, 2 hidden nodes in a second layer between the input A and output B, and in a third layer one output node. This is called a (4, 2, 1) network. The spectral data are presented as inputs A to the input nodes, wherein the product quality information B is the output. As known to those skilled in the art the nodes possess certain weights of interconnections, and may be biased. The weights and biases of the network can be stored and used to analyze input data comprising the measured infra-red absorbances and correlate the pattern to the octane number of a gasoline. Thus, for a prediction which utilizes the network algorithm to describe octane number from (N. )I.R. -data, important parameters, having been trained and successfully tested against the validation set, are the weights of interconnection between the nodes and the biases at the hidden and output nodes. These can be interrogated and then implemented in the network algorithm for the octane number analysis of future fuel samples. For multiple outputs, a neural network algorithm is implemented for each output. The implementation is by software code on a microprocessor chip, and is therefore flexible to any changes in network parameters which can be easily re-programmed. In addition to unleaded motor gasoline, the instrument can produce results for leaded fuels, provided that the lead content is known. A simple numerical correction can be added to the octane number predicted. The neural network can also be applied advantageously to provide octane numbers of samples from intermediate process streams e.g. catalytic cracking, reformate, isomerate, alkylate. These can be obtained using a single neural network trained on samples from the process streams. It will be appreciated by those skilled in the art that the network architectures applied may vary in the precise number of nodes that are present in each layer, or even in the number of actual layers. Advantageously, 2 to 5 layers have been applied. According to the invention advantageously the number of nodes of the input layer ranges from 3-10, the number of nodes of the hidden layer(s) ranges from 1-10, and the number of nodes of the output layer ranges from 1-3. More in particular, (3, 5, 1), (6, 6, 3) and (6, 6, 6, 3) networks could be applied. Various modifications of the invention will become apparent to those skilled in the art from the foregoing description and accompanying drawings. Such modifications are intended to fall within the scope of the appended claims.

Claims: C L A I M S 1. A method for data processing and optimisation of a neural network for application in the determination of physical property data of hydrocarbon products from measured (N.)I.R. spectral absorbances, characterized by the steps of: a) measuring the (N.)I.R. spectra of a large set of hydrocarbon product samples from a wide range of sources; b) selecting an overtone (harmonic) region of the (N.)I.R. spectra, thus obtained; c) selecting a number of discrete wavelengths in each (N.)I.R. spectrum, converting a number of the said wavelengths to absorption data and using said absorption data as an input to a neural network; d) training the neural network on the entire data set by repeated presentation of inputs and known outputs i.e. the (near- )infra-red data for the hydrocarbon product and its relevant physical property data, to learn the relationship between the two, and monitoring the performance of its predictions against the actual relevant physical property data as measured by standard methods for the training data, thus correlating the absorbance values with said relevant physical property; e) generating a set of values of the interconnection weights and biases of the network as adjusted after the learning period of step d) ; and f) applying these adjusted values, utilizing the network algorithm to (near- )infra-red spectra, taken under the same conditions, for hydrocarbon products of unknown physical property data. 2. The method as claimed in claim 1, characterized in that the spectral range is 0.70-30 μm wavelength. 3. The method as claimed in claim 1 or 2, characterized in that the overtone (harmonic) region is the second overtone (harmonic) region. 4. The method as claimed in claim 2 or 3, characterized in that the region of the (N.)I.R. spectrum covers 900-1300 n (wavelength) . 5. The method as claimed in any one of claims 1-4, characterized in that the number of selected discrete wavelengths is 5 for fuels that do not contain alcohols as oxygenates or do not include cetane ignition additions. 6. The method as claimed in any one of claims 1-4, characterized in that the number of selected discrete wavelengths is 6 for fuels that do contain alcohols as oxygenates or do include cetane ignition improver additives. 7. The method as claimed in any one of claims 1-6, characterized in that the number of layers of the neural network is 2 to 5. 8. The method as claimed in any one of claims 1-7, characterized in that the neural network applied has a three-layer or four-layer architecture. . The method as claimed in claim 8, characterized in that the number of nodes of the input layer is from 3 to 10, the number of nodes of the hidden layer(s) is from 1 to 10, and the number of nodes of the output layer is from 1 to 3. 10. The method as claimed in claim 8 or 9, characterized in that the network comprises 4 input nodes, 2 hidden nodes and one output node ((4, 2, 1) network). 11. The method as claimed in claim 8 or 9, characterized in that the network is a (3, 5, 1) network. 12. The method as claimed in claim 8 or 9, characterized in that the network is a (6, 6, 3) network. 13. The method as claimed in claim 8 or 9, characterized in that the network is a (6, 6, 6, 3) network. 14. The method as claimed in any one of claims 1-13, characterized in that the physical property is octane number or cetane number. 15. The method as claimed in any one of claims 1-13, characterized in that the physical property is vapour pressure. 16. The method as claimed in any one of claims 1-13, characterized in that the physical property is density. 17. The method as claimed in any one of claims 1-16, characterized in that the hydrocarbon product is gasoline or Diesel oil.

Inventors: BOYD ANDREW, TOLCHARD JOHN MICHAEL, BOYD, ANDREW, TOLCHARD, JOHN, MICHAEL
Applicants: SHELL CANADA LTD, SHELL INT RESEARCH, SHELL CANADA LIMITED, SHELL INTERNATIONALE RESEARCH MAATSCHAPPIJ B.V.
Application year: 1994
Main classification: G01N21
IPC codes: G01N21, G01N33